Analysis and Strategies of Internet + Taxation Risk Management of Listed Companies in the Big Data Era From the Organizational Psychology Perspective

The massive amount of information brought about by the era of big data has enormous potential value. In-depth discussion and analysis of solving the information asymmetry between tax collection and taxpayers are keys. This paper provides an in-depth study and analysis of the fiscal and tax intellige...

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Bibliographic Details
Published inFrontiers in psychology Vol. 13; p. 904417
Main Author Zhao, Xuan
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 24.06.2022
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Summary:The massive amount of information brought about by the era of big data has enormous potential value. In-depth discussion and analysis of solving the information asymmetry between tax collection and taxpayers are keys. This paper provides an in-depth study and analysis of the fiscal and tax intelligent risk management strategies of listed companies in the big data environment. The tax risk management of listed companies is optimized. Tax authorities should follow the development trend of big data, apply big data thinking, comprehensively excavate and utilize tax-related information, and improve the tax risk management model. At the same time, they should make full use of information technology to solve the problem of information asymmetry between tax collection and taxpayers and improve the quality and efficiency of tax risk management. The work discusses the problems of the current tax collection and management model in terms of management thinking, information collection, data analysis, legal system, and talent technology, and analyses the challenges brought by big data technology to the tax collection and management model as well as tax source management and tax informatization.
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This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology
Reviewed by: Yangxu Zhou, Jilin University, China; Jing Chen, China Pharmaceutical University, China
Edited by: Sang-Bing Tsai, Wuyi University, China
ISSN:1664-1078
1664-1078
DOI:10.3389/fpsyg.2022.904417